Ideas:
| Plant | Flowers | Date | lon | lat | ele | Month | Year | julian |
|---|---|---|---|---|---|---|---|---|
| Glossoloma oblongicalyx | 4 | 2015-10-19 | -78.59093 | 0.130838 | 2270 | October | 2015 | 292 |
| Gasteranthus quitensis | 2 | 2016-10-17 | -78.59770 | 0.120070 | 1940 | October | 2016 | 291 |
| Kohleria affinis | 1 | 2016-12-13 | -78.59534 | 0.126746 | 2110 | December | 2016 | 348 |
| Columnea ciliata | 3 | 2014-02-27 | -78.59934 | 0.116682 | 1960 | February | 2014 | 58 |
| Columnea medicinalis | 1 | 2014-04-23 | -78.59372 | 0.128700 | 2130 | April | 2014 | 113 |
| Drymonia teuscheri | 3 | 2016-07-28 | -78.59245 | 0.129393 | 2200 | July | 2016 | 210 |
NEEDS TO BE FIXED.
As range
Equal probability of flowering at anytime.
## sink("model/threshold_baseline.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- alpha[Plant[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<-alpha[PredPlant[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
##
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3164
## Total graph size: 15848
##
## Initializing model
## sink("model/threshold_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 1
## omega =1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19808
##
## Initializing model
## sink("model/threshold_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 1
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19807
##
## Initializing model
## sink("model/threshold_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 1
## omega =1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19964
##
## Initializing model
## sink("model/threshold_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 1
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19963
##
## Initializing model
## sink("model/threshold_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 1
## omega =1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19964
##
## Initializing model
## sink("model/threshold_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 1
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19963
##
## Initializing model
# One example
Glossoloma purpureum
## # A tibble: 7 x 2
## Model p
## <chr> <dbl>
## 1 baseline 0.542
## 2 interaction_attraction 0.986
## 3 interaction_repulsion 1
## 4 phylogenetic_attraction 0.994
## 5 phylogenetic_repulsion 1
## 6 trait_attraction 0.986
## 7 trait_repulsion 1
Without baseline
## # A tibble: 6 x 2
## Model p
## <chr> <dbl>
## 1 interaction_attraction 0.986
## 2 interaction_repulsion 1
## 3 phylogenetic_attraction 0.994
## 4 phylogenetic_repulsion 1
## 5 trait_attraction 0.986
## 6 trait_repulsion 1
| Model | mean | lower | upper |
|---|---|---|---|
| interaction_repulsion | 0.3322669 | 0.3248311 | 0.3400111 |
| trait_repulsion | 0.3321255 | 0.3247124 | 0.3398503 |
| phylogenetic_repulsion | 0.3240748 | 0.3165203 | 0.3316409 |
| trait_attraction | 0.2216292 | 0.2140343 | 0.2287572 |
| interaction_attraction | 0.2214639 | 0.2146735 | 0.2286750 |
| phylogenetic_attraction | 0.2179361 | 0.2106032 | 0.2249086 |
| Model | mean | lower | upper |
|---|---|---|---|
| interaction_repulsion | 0.3467874 | 0.3171927 | 0.3787375 |
| trait_repulsion | 0.3465017 | 0.3172757 | 0.3770764 |
| phylogenetic_repulsion | 0.3386877 | 0.3105482 | 0.3687708 |
| baseline | 0.2424153 | 0.2191860 | 0.2675249 |
| interaction_attraction | 0.2255914 | 0.2026578 | 0.2508306 |
| phylogenetic_attraction | 0.2245349 | 0.2026578 | 0.2491694 |
| trait_attraction | 0.2241495 | 0.2009967 | 0.2475914 |
| Plant | Month | Observed | baseline | interaction_attraction | interaction_repulsion | phylogenetic_attraction | phylogenetic_repulsion | trait_attraction | trait_repulsion |
|---|---|---|---|---|---|---|---|---|---|
| Besleria solanoides | Apr | 13.3 | 15.8 | 15.8 | 36.0 | 17.8 | 32.3 | 15.3 | 35.9 |
| Besleria solanoides | Aug | 5.9 | 16.3 | 14.5 | 35.5 | 16.5 | 32.8 | 14.9 | 35.5 |
| Besleria solanoides | Dec | 12.5 | 16.6 | 15.0 | 36.1 | 18.1 | 33.0 | 15.9 | 36.5 |
| Besleria solanoides | Feb | 0.0 | 16.2 | 15.1 | 34.3 | 16.4 | 31.3 | 15.7 | 35.3 |
| Besleria solanoides | Jan | 0.0 | 16.4 | 16.8 | 37.7 | 19.0 | 32.2 | 17.4 | 37.6 |
| Besleria solanoides | Jul | 5.6 | 17.0 | 14.5 | 33.7 | 15.7 | 30.1 | 14.8 | 34.0 |
| Besleria solanoides | Jun | 5.0 | 16.5 | 14.7 | 34.5 | 16.4 | 32.0 | 14.2 | 35.1 |
| Besleria solanoides | Mar | 0.0 | 16.6 | 17.6 | 37.8 | 18.0 | 32.9 | 17.5 | 35.6 |
| Besleria solanoides | May | 0.0 | 16.3 | 15.4 | 36.6 | 16.8 | 32.5 | 15.5 | 36.4 |
| Besleria solanoides | Nov | 33.3 | 16.5 | 16.0 | 34.7 | 16.7 | 32.5 | 16.5 | 37.4 |
| Besleria solanoides | Oct | 55.6 | 16.5 | 16.3 | 37.0 | 17.1 | 34.0 | 16.2 | 36.2 |
| Besleria solanoides | Sep | 50.0 | 16.0 | 15.2 | 34.9 | 16.9 | 31.3 | 15.7 | 35.5 |
| Columnea ciliata | Apr | 46.7 | 15.5 | 16.2 | 40.7 | 18.7 | 34.7 | 16.6 | 38.7 |
| Columnea ciliata | Aug | 0.0 | 16.3 | 14.2 | 33.2 | 14.0 | 27.7 | 14.0 | 31.5 |
| Columnea ciliata | Dec | 0.0 | 16.0 | 13.8 | 36.0 | 16.5 | 30.6 | 14.8 | 34.9 |
| Columnea ciliata | Feb | 43.8 | 15.8 | 15.3 | 35.8 | 16.2 | 32.1 | 15.2 | 37.0 |
| Columnea ciliata | Jan | 25.0 | 15.7 | 16.6 | 40.9 | 19.2 | 36.0 | 16.5 | 41.1 |
| Columnea ciliata | Jul | 5.6 | 15.2 | 16.0 | 37.1 | 17.1 | 32.9 | 15.7 | 36.8 |
| Columnea ciliata | Jun | 10.0 | 16.3 | 15.8 | 36.2 | 16.5 | 32.3 | 15.8 | 35.9 |
| Columnea ciliata | Mar | 66.7 | 17.2 | 15.3 | 40.6 | 18.6 | 35.6 | 16.5 | 40.9 |
| Columnea ciliata | May | 5.9 | 16.4 | 15.4 | 36.6 | 16.5 | 33.9 | 15.4 | 37.1 |
| Columnea ciliata | Nov | 0.0 | 15.6 | 16.1 | 35.6 | 17.6 | 31.8 | 15.8 | 36.0 |
| Columnea ciliata | Oct | 0.0 | 16.3 | 14.6 | 34.1 | 15.7 | 30.6 | 14.3 | 33.7 |
| Columnea ciliata | Sep | 0.0 | 15.8 | 15.5 | 36.4 | 16.3 | 31.4 | 15.5 | 35.7 |
| Columnea kucyniakii | Apr | 6.7 | 8.2 | 11.5 | 30.0 | 12.1 | 23.5 | 11.3 | 30.2 |
| Columnea kucyniakii | Aug | 0.0 | 8.3 | 12.1 | 30.3 | 12.3 | 25.0 | 11.3 | 31.3 |
| Columnea kucyniakii | Dec | 12.5 | 8.9 | 12.4 | 31.9 | 13.3 | 25.4 | 13.3 | 30.5 |
| Columnea kucyniakii | Feb | 43.8 | 8.1 | 11.7 | 31.2 | 11.7 | 24.1 | 11.7 | 32.0 |
| Columnea kucyniakii | Jan | 25.0 | 8.6 | 12.1 | 32.4 | 13.4 | 25.1 | 12.0 | 32.5 |
| Columnea kucyniakii | Jul | 0.0 | 8.7 | 11.4 | 30.6 | 11.3 | 23.8 | 11.6 | 30.2 |
| Columnea kucyniakii | Jun | 0.0 | 8.5 | 12.2 | 30.5 | 11.6 | 24.3 | 11.8 | 29.6 |
| Columnea kucyniakii | Mar | 16.7 | 8.8 | 12.3 | 31.5 | 13.3 | 25.9 | 12.0 | 32.5 |
| Columnea kucyniakii | May | 0.0 | 8.4 | 12.6 | 32.7 | 13.3 | 25.9 | 12.5 | 32.2 |
| Columnea kucyniakii | Nov | 11.1 | 8.7 | 10.1 | 29.3 | 12.5 | 22.2 | 10.7 | 29.2 |
| Columnea kucyniakii | Oct | 0.0 | 8.3 | 11.0 | 29.8 | 12.4 | 23.8 | 10.8 | 30.8 |
| Columnea kucyniakii | Sep | 0.0 | 8.6 | 11.8 | 31.7 | 12.2 | 25.5 | 12.4 | 31.8 |
| Columnea medicinalis | Apr | 0.0 | 14.5 | 15.4 | 32.1 | 16.5 | 29.9 | 15.5 | 33.4 |
| Columnea medicinalis | Aug | 23.5 | 13.9 | 17.7 | 35.5 | 19.0 | 31.5 | 16.3 | 35.7 |
| Columnea medicinalis | Dec | 0.0 | 14.7 | 17.2 | 37.1 | 18.9 | 34.3 | 17.1 | 35.9 |
| Columnea medicinalis | Feb | 12.5 | 14.7 | 14.7 | 32.2 | 16.1 | 28.8 | 15.8 | 35.1 |
| Columnea medicinalis | Jan | 16.7 | 14.1 | 15.3 | 34.0 | 17.0 | 30.6 | 14.8 | 33.2 |
| Columnea medicinalis | Jul | 27.8 | 14.6 | 15.2 | 31.3 | 15.4 | 28.7 | 15.0 | 31.5 |
| Columnea medicinalis | Jun | 20.0 | 14.1 | 16.2 | 33.8 | 17.0 | 30.6 | 15.7 | 33.7 |
| Columnea medicinalis | Mar | 8.3 | 14.4 | 15.0 | 32.9 | 17.7 | 30.6 | 14.8 | 34.4 |
| Columnea medicinalis | May | 11.8 | 14.5 | 16.0 | 35.3 | 18.4 | 32.4 | 15.4 | 35.6 |
| Columnea medicinalis | Nov | 11.1 | 14.5 | 16.4 | 34.3 | 17.5 | 31.4 | 16.6 | 34.8 |
| Columnea medicinalis | Oct | 22.2 | 14.4 | 16.4 | 34.8 | 17.6 | 31.2 | 16.2 | 35.5 |
| Columnea medicinalis | Sep | 5.0 | 14.2 | 15.2 | 34.3 | 15.9 | 30.3 | 15.9 | 33.7 |
| Columnea picta | Apr | 13.3 | 14.3 | 13.7 | 38.0 | 14.1 | 36.5 | 13.3 | 37.9 |
| Columnea picta | Aug | 5.9 | 13.6 | 11.3 | 32.0 | 11.9 | 31.2 | 11.5 | 31.3 |
| Columnea picta | Dec | 12.5 | 12.8 | 12.9 | 34.6 | 13.1 | 35.5 | 13.5 | 34.0 |
| Columnea picta | Feb | 6.2 | 13.8 | 13.0 | 35.9 | 13.6 | 35.5 | 12.3 | 36.2 |
| Columnea picta | Jan | 16.7 | 13.7 | 13.5 | 38.1 | 13.0 | 36.7 | 13.1 | 37.4 |
| Columnea picta | Jul | 16.7 | 14.3 | 14.0 | 38.4 | 14.6 | 37.0 | 13.6 | 38.0 |
| Columnea picta | Jun | 25.0 | 13.3 | 13.1 | 36.2 | 13.4 | 35.5 | 12.9 | 36.0 |
| Columnea picta | Mar | 0.0 | 13.7 | 12.9 | 36.3 | 13.5 | 36.0 | 13.0 | 36.0 |
| Columnea picta | May | 35.3 | 14.6 | 14.7 | 40.5 | 15.1 | 40.2 | 15.1 | 40.5 |
| Columnea picta | Nov | 0.0 | 13.7 | 10.4 | 28.7 | 11.5 | 28.6 | 10.4 | 28.4 |
| Columnea picta | Oct | 0.0 | 14.3 | 9.8 | 29.1 | 11.2 | 28.1 | 10.1 | 27.7 |
| Columnea picta | Sep | 20.0 | 14.0 | 14.4 | 38.7 | 14.6 | 37.3 | 14.1 | 38.6 |
| Columnea strigosa | Apr | 0.0 | 12.4 | 14.4 | 29.6 | 12.7 | 33.1 | 14.4 | 30.4 |
| Columnea strigosa | Aug | 11.8 | 11.8 | 16.7 | 36.2 | 15.0 | 36.9 | 17.7 | 36.4 |
| Columnea strigosa | Dec | 0.0 | 12.3 | 14.6 | 29.2 | 12.6 | 32.3 | 15.5 | 30.4 |
| Columnea strigosa | Feb | 12.5 | 12.1 | 15.0 | 31.7 | 13.7 | 34.4 | 15.6 | 33.1 |
| Columnea strigosa | Jan | 8.3 | 12.5 | 12.7 | 27.4 | 11.3 | 29.6 | 12.8 | 26.3 |
| Columnea strigosa | Jul | 27.8 | 12.4 | 15.0 | 33.9 | 14.7 | 35.8 | 15.3 | 34.1 |
| Columnea strigosa | Jun | 40.0 | 12.0 | 15.6 | 33.4 | 14.2 | 35.3 | 15.5 | 32.9 |
| Columnea strigosa | Mar | 0.0 | 12.3 | 12.7 | 27.5 | 11.8 | 28.4 | 13.5 | 27.4 |
| Columnea strigosa | May | 23.5 | 12.5 | 13.9 | 29.0 | 12.9 | 30.1 | 13.6 | 27.4 |
| Columnea strigosa | Nov | 0.0 | 12.3 | 16.1 | 33.8 | 13.8 | 35.1 | 16.2 | 33.6 |
| Columnea strigosa | Oct | 0.0 | 13.2 | 16.2 | 34.0 | 14.5 | 36.0 | 15.7 | 33.9 |
| Columnea strigosa | Sep | 0.0 | 13.4 | 14.2 | 32.2 | 13.6 | 33.3 | 14.1 | 31.8 |
| Drymonia collegarum | Apr | 13.3 | 14.0 | 14.9 | 35.2 | 14.8 | 34.0 | 15.0 | 34.8 |
| Drymonia collegarum | Aug | 17.6 | 13.2 | 17.0 | 36.6 | 16.0 | 37.0 | 16.8 | 36.9 |
| Drymonia collegarum | Dec | 25.0 | 13.2 | 16.0 | 39.4 | 15.8 | 37.5 | 15.8 | 38.8 |
| Drymonia collegarum | Feb | 6.2 | 13.0 | 14.3 | 34.5 | 14.1 | 34.5 | 14.3 | 33.9 |
| Drymonia collegarum | Jan | 16.7 | 13.5 | 14.5 | 37.1 | 16.7 | 36.9 | 14.3 | 37.1 |
| Drymonia collegarum | Jul | 11.1 | 13.6 | 13.6 | 33.3 | 13.4 | 32.0 | 13.4 | 32.7 |
| Drymonia collegarum | Jun | 15.0 | 13.7 | 14.9 | 34.8 | 14.0 | 33.8 | 15.6 | 34.7 |
| Drymonia collegarum | Mar | 8.3 | 13.2 | 14.9 | 37.4 | 15.0 | 36.9 | 15.3 | 37.5 |
| Drymonia collegarum | May | 23.5 | 13.9 | 15.0 | 36.7 | 14.8 | 37.4 | 16.1 | 37.6 |
| Drymonia collegarum | Nov | 11.1 | 14.1 | 16.5 | 35.7 | 15.1 | 35.2 | 16.6 | 35.4 |
| Drymonia collegarum | Oct | 5.6 | 13.5 | 15.7 | 36.0 | 15.1 | 36.0 | 15.9 | 35.2 |
| Drymonia collegarum | Sep | 10.0 | 13.7 | 14.4 | 35.3 | 14.4 | 35.2 | 15.4 | 35.7 |
| Drymonia tenuis | Apr | 13.3 | 13.5 | 14.8 | 36.6 | 14.3 | 36.7 | 14.5 | 36.5 |
| Drymonia tenuis | Aug | 11.8 | 14.3 | 13.9 | 32.2 | 12.6 | 31.7 | 14.3 | 32.9 |
| Drymonia tenuis | Dec | 0.0 | 14.2 | 15.5 | 34.0 | 13.1 | 34.2 | 13.8 | 34.4 |
| Drymonia tenuis | Feb | 18.8 | 13.8 | 13.5 | 36.3 | 13.4 | 35.8 | 13.5 | 35.5 |
| Drymonia tenuis | Jan | 16.7 | 14.0 | 14.7 | 38.1 | 15.0 | 39.1 | 14.4 | 39.8 |
| Drymonia tenuis | Jul | 16.7 | 14.0 | 13.3 | 36.0 | 12.9 | 35.1 | 12.9 | 35.7 |
| Drymonia tenuis | Jun | 20.0 | 14.0 | 13.4 | 33.6 | 12.7 | 34.8 | 13.0 | 34.5 |
| Drymonia tenuis | Mar | 25.0 | 14.3 | 15.7 | 39.3 | 15.3 | 40.2 | 14.8 | 38.8 |
| Drymonia tenuis | May | 23.5 | 13.7 | 13.4 | 33.9 | 13.1 | 35.5 | 13.8 | 34.0 |
| Drymonia tenuis | Nov | 0.0 | 13.9 | 14.6 | 35.7 | 14.4 | 36.6 | 14.7 | 36.2 |
| Drymonia tenuis | Oct | 5.6 | 13.9 | 15.0 | 36.4 | 14.5 | 35.4 | 14.4 | 35.6 |
| Drymonia tenuis | Sep | 5.0 | 14.6 | 13.6 | 34.8 | 13.1 | 35.6 | 13.2 | 34.8 |
| Drymonia teuscheri | Apr | 26.7 | 17.1 | 15.7 | 37.6 | 16.1 | 36.9 | 14.4 | 36.8 |
| Drymonia teuscheri | Aug | 23.5 | 17.0 | 17.3 | 42.8 | 18.3 | 42.7 | 17.0 | 43.4 |
| Drymonia teuscheri | Dec | 0.0 | 16.4 | 17.0 | 41.3 | 17.6 | 39.3 | 15.9 | 39.8 |
| Drymonia teuscheri | Feb | 6.2 | 17.0 | 14.5 | 39.0 | 16.4 | 37.8 | 15.5 | 39.0 |
| Drymonia teuscheri | Jan | 16.7 | 16.7 | 13.6 | 34.1 | 14.4 | 35.0 | 14.3 | 34.2 |
| Drymonia teuscheri | Jul | 22.2 | 17.5 | 15.2 | 38.6 | 16.0 | 38.8 | 14.4 | 40.1 |
| Drymonia teuscheri | Jun | 15.0 | 16.8 | 15.8 | 40.6 | 17.0 | 39.0 | 15.5 | 39.4 |
| Drymonia teuscheri | Mar | 16.7 | 17.1 | 13.8 | 32.8 | 14.4 | 33.2 | 14.8 | 32.9 |
| Drymonia teuscheri | May | 29.4 | 16.6 | 14.6 | 37.0 | 15.0 | 37.2 | 16.5 | 36.2 |
| Drymonia teuscheri | Nov | 11.1 | 16.7 | 16.4 | 41.2 | 17.7 | 40.9 | 16.2 | 41.5 |
| Drymonia teuscheri | Oct | 11.1 | 17.2 | 16.3 | 41.7 | 18.0 | 40.3 | 16.2 | 40.8 |
| Drymonia teuscheri | Sep | 10.0 | 16.5 | 15.3 | 38.4 | 15.8 | 37.3 | 16.4 | 38.1 |
| Gasteranthus lateralis | Apr | 13.3 | 13.6 | 13.6 | 33.5 | 14.2 | 34.9 | 13.9 | 34.2 |
| Gasteranthus lateralis | Aug | 17.6 | 13.6 | 15.2 | 37.7 | 16.4 | 37.2 | 14.3 | 37.6 |
| Gasteranthus lateralis | Dec | 25.0 | 14.1 | 15.0 | 36.0 | 17.1 | 34.6 | 15.3 | 36.9 |
| Gasteranthus lateralis | Feb | 0.0 | 14.0 | 13.4 | 34.6 | 14.6 | 35.8 | 14.1 | 36.1 |
| Gasteranthus lateralis | Jan | 33.3 | 13.2 | 14.3 | 33.3 | 13.2 | 33.6 | 14.7 | 32.5 |
| Gasteranthus lateralis | Jul | 0.0 | 14.0 | 13.1 | 35.2 | 14.5 | 34.8 | 13.2 | 35.0 |
| Gasteranthus lateralis | Jun | 0.0 | 14.5 | 13.7 | 35.5 | 14.5 | 35.9 | 13.6 | 35.8 |
| Gasteranthus lateralis | Mar | 8.3 | 14.4 | 14.3 | 33.1 | 12.5 | 33.6 | 13.8 | 32.4 |
| Gasteranthus lateralis | May | 5.9 | 13.8 | 13.3 | 34.3 | 14.4 | 34.9 | 13.4 | 35.2 |
| Gasteranthus lateralis | Nov | 44.4 | 14.4 | 13.6 | 36.9 | 16.4 | 36.5 | 15.5 | 36.2 |
| Gasteranthus lateralis | Oct | 33.3 | 13.9 | 15.1 | 36.1 | 16.6 | 37.5 | 14.9 | 36.7 |
| Gasteranthus lateralis | Sep | 10.0 | 14.2 | 14.1 | 35.1 | 14.0 | 36.1 | 13.6 | 35.0 |
| Gasteranthus quitensis | Apr | 0.0 | 11.6 | 15.4 | 30.4 | 13.5 | 33.1 | 15.0 | 29.7 |
| Gasteranthus quitensis | Aug | 17.6 | 12.4 | 13.8 | 28.5 | 12.0 | 30.2 | 13.9 | 28.6 |
| Gasteranthus quitensis | Dec | 12.5 | 12.8 | 13.5 | 27.8 | 12.5 | 30.6 | 13.9 | 28.7 |
| Gasteranthus quitensis | Feb | 18.8 | 11.9 | 14.4 | 31.3 | 13.4 | 34.0 | 15.7 | 31.1 |
| Gasteranthus quitensis | Jan | 33.3 | 11.8 | 16.8 | 33.3 | 13.9 | 35.9 | 17.4 | 33.1 |
| Gasteranthus quitensis | Jul | 5.6 | 12.2 | 13.7 | 31.4 | 12.8 | 33.0 | 14.4 | 30.9 |
| Gasteranthus quitensis | Jun | 0.0 | 11.8 | 13.9 | 29.6 | 11.6 | 31.9 | 14.0 | 29.5 |
| Gasteranthus quitensis | Mar | 0.0 | 12.1 | 16.2 | 33.5 | 13.7 | 36.1 | 16.7 | 33.6 |
| Gasteranthus quitensis | May | 0.0 | 11.8 | 14.2 | 30.0 | 12.3 | 32.4 | 14.6 | 30.3 |
| Gasteranthus quitensis | Nov | 11.1 | 11.3 | 15.6 | 28.7 | 13.4 | 31.6 | 16.0 | 29.0 |
| Gasteranthus quitensis | Oct | 22.2 | 12.2 | 15.7 | 30.4 | 13.3 | 32.1 | 16.4 | 30.3 |
| Gasteranthus quitensis | Sep | 20.0 | 12.0 | 15.1 | 31.0 | 12.5 | 32.9 | 14.5 | 30.8 |
| Glossoloma oblongicalyx | Apr | 0.0 | 17.9 | 16.2 | 37.7 | 16.8 | 37.3 | 16.2 | 37.7 |
| Glossoloma oblongicalyx | Aug | 35.3 | 19.2 | 17.8 | 45.1 | 20.0 | 43.6 | 18.1 | 45.6 |
| Glossoloma oblongicalyx | Dec | 0.0 | 17.4 | 17.2 | 39.6 | 16.7 | 38.7 | 17.2 | 40.2 |
| Glossoloma oblongicalyx | Feb | 0.0 | 19.0 | 16.6 | 40.7 | 17.6 | 40.8 | 17.1 | 40.7 |
| Glossoloma oblongicalyx | Jan | 0.0 | 18.6 | 13.4 | 33.2 | 14.7 | 33.2 | 14.6 | 32.9 |
| Glossoloma oblongicalyx | Jul | 38.9 | 18.6 | 17.4 | 41.8 | 17.7 | 41.3 | 17.8 | 42.9 |
| Glossoloma oblongicalyx | Jun | 10.0 | 18.0 | 17.0 | 41.1 | 18.4 | 41.2 | 17.0 | 41.6 |
| Glossoloma oblongicalyx | Mar | 0.0 | 18.0 | 13.9 | 33.4 | 14.3 | 34.8 | 14.8 | 34.7 |
| Glossoloma oblongicalyx | May | 11.8 | 18.7 | 15.6 | 35.2 | 15.8 | 34.5 | 15.8 | 36.0 |
| Glossoloma oblongicalyx | Nov | 22.2 | 18.9 | 17.0 | 43.6 | 19.5 | 42.6 | 17.1 | 44.3 |
| Glossoloma oblongicalyx | Oct | 38.9 | 18.2 | 16.4 | 43.7 | 18.2 | 42.9 | 16.8 | 44.5 |
| Glossoloma oblongicalyx | Sep | 35.0 | 17.9 | 16.3 | 38.8 | 16.7 | 38.4 | 16.5 | 39.2 |
| Glossoloma purpureum | Apr | 6.7 | 13.1 | 15.5 | 36.9 | 14.9 | 36.8 | 15.6 | 36.2 |
| Glossoloma purpureum | Aug | 17.6 | 13.2 | 13.9 | 31.4 | 13.7 | 30.1 | 14.4 | 31.0 |
| Glossoloma purpureum | Dec | 12.5 | 14.2 | 14.3 | 34.7 | 14.3 | 34.0 | 14.9 | 35.4 |
| Glossoloma purpureum | Feb | 12.5 | 13.2 | 15.2 | 34.8 | 13.8 | 34.9 | 14.3 | 35.1 |
| Glossoloma purpureum | Jan | 16.7 | 13.0 | 14.2 | 35.6 | 13.2 | 35.2 | 13.8 | 35.2 |
| Glossoloma purpureum | Jul | 22.2 | 13.1 | 16.2 | 36.9 | 14.3 | 37.0 | 15.3 | 36.3 |
| Glossoloma purpureum | Jun | 10.0 | 13.6 | 15.1 | 35.5 | 14.7 | 35.0 | 14.8 | 35.6 |
| Glossoloma purpureum | Mar | 0.0 | 13.0 | 14.1 | 35.4 | 13.7 | 35.1 | 14.0 | 36.5 |
| Glossoloma purpureum | May | 5.9 | 13.5 | 15.9 | 40.6 | 15.4 | 40.7 | 16.5 | 41.3 |
| Glossoloma purpureum | Nov | 11.1 | 12.9 | 12.8 | 27.1 | 13.6 | 27.9 | 12.5 | 28.1 |
| Glossoloma purpureum | Oct | 11.1 | 13.7 | 12.1 | 27.8 | 13.0 | 27.1 | 12.6 | 27.8 |
| Glossoloma purpureum | Sep | 25.0 | 13.5 | 15.4 | 37.8 | 14.8 | 38.0 | 15.3 | 37.7 |
| Kohleria affinis | Apr | 46.7 | 15.5 | 14.3 | 36.7 | 14.6 | 36.1 | 15.1 | 36.6 |
| Kohleria affinis | Aug | 5.9 | 15.5 | 15.4 | 38.4 | 14.7 | 37.9 | 15.5 | 38.3 |
| Kohleria affinis | Dec | 12.5 | 15.5 | 16.1 | 38.8 | 15.8 | 35.9 | 15.9 | 39.1 |
| Kohleria affinis | Feb | 25.0 | 15.7 | 14.2 | 36.9 | 14.0 | 36.1 | 14.5 | 36.0 |
| Kohleria affinis | Jan | 8.3 | 16.0 | 15.1 | 38.3 | 14.4 | 37.4 | 15.1 | 37.3 |
| Kohleria affinis | Jul | 0.0 | 15.8 | 13.8 | 36.1 | 14.4 | 33.9 | 14.3 | 35.0 |
| Kohleria affinis | Jun | 15.0 | 15.8 | 14.6 | 36.9 | 15.6 | 36.1 | 14.8 | 36.4 |
| Kohleria affinis | Mar | 41.7 | 14.7 | 15.1 | 38.0 | 13.5 | 37.0 | 15.8 | 37.6 |
| Kohleria affinis | May | 29.4 | 15.2 | 16.0 | 37.4 | 16.4 | 37.6 | 16.2 | 37.2 |
| Kohleria affinis | Nov | 0.0 | 15.9 | 13.9 | 37.9 | 14.3 | 37.4 | 16.5 | 38.2 |
| Kohleria affinis | Oct | 0.0 | 16.0 | 15.4 | 38.7 | 13.8 | 38.1 | 15.2 | 38.7 |
| Kohleria affinis | Sep | 5.0 | 15.4 | 14.5 | 36.8 | 15.1 | 36.0 | 14.5 | 36.4 |